Data structures and network algorithms
Data structures and network algorithms
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Improving Microaggregation for Complex Record Anonymization
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Towards the evaluation of time series protection methods
Information Sciences: an International Journal
Constrained Microaggregation: Adding Constraints for Data Editing
Transactions on Data Privacy
A new framework to automate constrained microaggregation
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Suppressing microdata to prevent classification based inference
The VLDB Journal — The International Journal on Very Large Data Bases
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Microaggregation for database and location privacy
NGITS'06 Proceedings of the 6th international conference on Next Generation Information Technologies and Systems
Optimal multivariate 2-microaggregation for microdata protection: a 2-approximation
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Information fusion in data privacy: A survey
Information Fusion
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Microaggregation is a technique for the protection of the confidentiality of respondents in microdata releases. It is used for economic data where respondent identifiability is high. Microaggregation releases the averages of small groups in which no single respondent is dominant. It was developed for univariate data. The data was sorted and the averages of adjacent fixed size groups were reported. The groups can be allowed to have varying sizes so that no group will include a large gap in the sorted data. The groups become more homogeneous when their boundaries are sensitive to the distribution of the data. This is like clustering but with the number of clusters chosen to be as large as possible subject to homogeneous clusters and a minimum cluster size. Approximate methods based on comparisons are developed. Exact methods based on linear optimization are also developed. For bivariate, or higher dimensional, data the notion of adjacency is defined even though sorting is no longer well defined. The constraints for minimum cluster size are also more elaborate and not so easily solved. We may also use only a triangulation to limit the number of adjacencies to be considered in the algorithms. Hybrids of the approximate and exact methods combine the strengths of each strategy.